Experiments into position bias in e-commerce settings have shown that customers are prejudiced towards the first few results [1]}. It is customary to rank products primarily based on the popularity of a product without taking semantics into account [2]}. However, user studies and analyses of interaction logs reveal that customers use various queries with subtle differences to search for the same product set that should lead to different rankings [3]}. Adding semantics, to query understanding and to product ranking, should result in a better ranking. From the point of view of generating semantically more meaningful ranked lists of products than purely ranking by popularity, our experiments in Section  suggest that we should consider models like ARC-I, MV-LSTM and DRMMTKS.
